Nanoscale profiling of evolving intermolecular interactions in ageing FUS condensates
Bibliographic record
Abstract
Abstract In addition to the native state, proteins can form liquid-like condensates, viscoelastic condensates, such as gels, as well as solid-like condensates, such as amyloid fibrils, crystals and amorphous materials. The material properties of these condensates play important roles in their cellular functions, with aberrant liquid-to-solid phase transitions having been implicated in neurodegenerative diseases. However, the molecular changes and resultant material properties across the whole phase space of condensates are complex and yet to be fully understood. The extreme sensitivity to their environment, which enables their biological function, is also what makes protein condensates particularly challenging experimental targets. Here, we provide a characterisation of the ageing behaviour of the full-length fused in sarcoma (FUS) protein. We achieve this goal by using a microfluidic sample deposition technology to enable the application of surface-based techniques to the study of biological condensates. We first demonstrate that we maintain relevant structural features of condensates in physiologically-relevant conditions on surfaces. Then, using a combination of atomic force microscopy and vibrational spectroscopy, we characterise the spatio-temporal changes in the structure and mechanical properties of the condensates to reveal local phase transitions in individual condensates. We observe that initially dynamic, fluid-like condensates undergo a global increase in elastic response conferred by an increase in the density of cation-π intermolecular interactions. Solid-like structures form first at condensate-solvent interfaces, before heterogeneously propagating throughout the aged fluid core. These solid structures are composed of heterogenous, non-amyloid β-sheets, which are stabilised by hydrogen-bonding interactions not observed in the fluid state. Overall, this study identifies the molecular conformations associated with different physical states of FUS condensates, establishing a technology platform to understand the role of phase behaviour in condensate function and dysfunction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".